Anchors play a crucial role in Faster R-CNN. Basically, an anchor is a box. Intuitively, we know that objects in an image should fit specific common aspect ratios and sizes. For instance, to detect people, rectangular boxes that resemble the shapes of humans are required. In such a way, Faster R-CNN proposed to use \(k\)  boxes having fixed sizes and aspect ratios, called indeed anchor boxes, that are slid over the image looking for objects. In the default configuration of Faster R-CNN, the authors suggested using nine anchors at a position of an image.
The architecture of Faster R-CNN is shown in fig:fasterrcnnarchitecture. The standard choice of anchors proposed by the authors of [1]} work well for many datasets. However, one has the freedom to design different kinds of anchors/boxes. For example, if the network is designed to detect pedestrians, it is better to consider short, big, or square boxes. A neat set of anchors may increase the speed as well as the accuracy.
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